Hardware Trojans classification for gate-level netlists based on machine learning

Hasegawa, Kento; Oya, Masaru; Yanagisawa, Masao; Togawa, Nozomu

doi:10.1109/iolts.2016.7604700

Cited by 120 publications

(76 citation statements)

References 8 publications

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“…Hasegawa et al [26] extracted 5 Trojan netlist features through the preliminary analysis of normal circuit and Trojan circuit and applied support vector machine(SVM) to the detection of HTs in gate-level netlists for the first time. The experimental results showed that the trained SVM classifier can achieve 100% TPR in some cases.…”

Section: B Gate-level Netlists Detection Based On Machine Learningmentioning

confidence: 99%

An Unsupervised Detection Approach for Hardware Trojans

et al. 2020

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With the booming development of the cyber-physical system, human society is much more dependent on information technology. Unfortunately, like software, hardware is not trusted at all, due to so many third parties involved in the separated integrated circuit's (IC) design and manufacturing stages for the high profit. The malicious circuits (named Hardware Trojans) can be implanted during any stage of the ICs' design and manufacturing process. However, the existing pre-silicon approaches based on machine learning theory have good performance, they all belong to supervised learning methods, which have a key prerequisite that is numerous already known information. Meanwhile, hardware Trojans are even more unimaginable because today's ICs are becoming more complicated. The known information is even harder to gain. Furthermore, the training process for supervised learning methods tends to be time-consuming and generally requires a huge amount of balanced training data. Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD. We firstly visualize the distribution features of normal nets and Trojan nets, and then reveal the differences between the two types of nets to reduce the dimension of the feature set. According to the outliers of each net, the abnormal nets are selected and verified by professionals later to confirm whether it is a true Trojan relative to the host circuit to realize the detection. The experiments show that the proposed method can detect hardware Trojans effectively and reduce the cost of manual secondary detection. For the Trust-HUB benchmarks, the PL-HTD achieves up to 73.08% TPR and 97.52% average TNR, moreover, it achieves average 96.00% accuracy, which shows the feasibility and efficiency of hardware Trojans detecting by employing a method without the guidance of class label information. INDEX TERMS hardware security, hardware Trojan detection, integrated circuit, unsupervised machine learning, LOF

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Section: B Gate-level Netlists Detection Based On Machine Learningmentioning

confidence: 99%

An Unsupervised Detection Approach for Hardware Trojans

et al. 2020

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“…Based on the outcome of these parameters, classification of the circuit under test is done using different classifiers such as support vector machines, random forest classifiers etc. The different types of hardware Trojans and its threat models are analyzed in [9][10][11]. In [11], hardware Trojan nets are identified by the proposed Support vector machine based classifier.…”

Section: Related Workmentioning

confidence: 99%

“…The different types of hardware Trojans and its threat models are analyzed in [9][10][11]. In [11], hardware Trojan nets are identified by the proposed Support vector machine based classifier. The static detection process is discussed her, which does not require any test patterns for activating the Trojan model and also avoids the use of reference golden chip for the analysis.…”

Section: Related Workmentioning

confidence: 99%

“…The existing machine learning technique are classified into supervised and unsupervised learning. The supervised learning algorithm like SVM based classifier [11], Multilayer neural network [13] method and the unsupervised learning algorithm like boundary net structure [15], PL-HTD [16] are compared with deep learning scheme. The proposed deep learning technique are more focused, since it overcomes the drawback of the machine learning algorithm and it also improves the performance metric.…”

Section: Comparision With Existing Techniquesmentioning

confidence: 99%

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An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

Murugesan¹,

Manickam²

2020

IJIES

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In modern electronic design, hardware Trojan has emerged as a major threat in the hardware security. To detect the hardware Trojan is a major problem in testing process because of their inherent concealed nature. In this work, we propose a deep learning-based Trojan classification approach, which extracts the optimal feature to indicate the nets affected by the Trojan module. In this approach, a handcrafted algorithm along with the structural report is also analyzed for extracting further features of the gate level netlist, which stamp out the requirement of golden chip. This detection technique is also validated using game theoretical approach, which is modelled as zero-sum game between the attacker and the defender. The Simulation is employed on ISCAS’85, ISCAS’89 and Trust-HUB circuits and the deep learning algorithm performs the best in detection and classification of Trojan type with an average True positive rate of 96.69% and an accuracy of 96.25%.

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“…Experimental results of existing machine-learningbased hardware-Trojan detection methods So far, some existing machine-learning hardware-Trojan detection methods are as follows: support vector machine (SVM)-based hardware-Trojan detection method, 22 neural network (NN)-based hardware-Trojan detection method, 23 random forest (RF)-based hardware-Trojan detection method, 24 and multi-layer neural network (MNN)-based hardware-Trojan detection method. 25 The true positive rate (TPR) and the true negative rate (TNR) are used by Hasegawa et al 22 and Inoue et al 26 to evaluate the detection results. In addition to the TPR and the TNR, Hasegawa et al 24 also used the Accuracy, the Precision, and the F-measure to evaluate the detection results and proposed that the F-measure is the best to measure the results very well.…”

Section: Hardware-trojan Detection Process For Gate-level Netlistmentioning

confidence: 99%

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

Dong

Chen

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall, 86.75% average F-measure, and 99.83% average Accuracy, which is the best detection result among existing machine-learning-based hardware-Trojan detection methods.

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Hardware Trojans classification for gate-level netlists based on machine learning

Cited by 120 publications

References 8 publications

An Unsupervised Detection Approach for Hardware Trojans

An Unsupervised Detection Approach for Hardware Trojans

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

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